Curricula

Objectives

Course objectives
In the course, you will learn how to use advanced new technologies, such as artificial intelligence, in the implementation of IoT systems. You get to know different neural networks and understand their basic parameters.

Competences:
EUR-ACE Knowledge and Understanding
EUR-ACE Engineering Practice


You know the architectures of neural networks and understand their application areas. You know how to make a Q-table of basic neural network design and implementation in practice. You know what the RL agent does and you know how to train the agent to make it work optimally. You understand the difference between On-Policy and Off-Policy algorithms and know how to use information to select an algorithm.

Content

Comparison of neural networks (RL, Supervised, Unsupervised )
Keras, Pytorch and other algorithm-based neural networks
RL agent and environment
Exploitation and exploration
Reward and hyperparameters
Basic neuromath and activation functions
Policy gradient method and value based learning
Bellman equation and its demo (Q-learning)
Pytorch, PPO algorithm and demo

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Avoidance 1: You understand the principles of neural networks and can apply the knowledge to select them

Satisfactory 2: You understand the basics of Q-learning methods and can make a small application with it

Evaluation criteria, good (3-4)

Good 3: Can adjust the parameter of the Bellman algorithm (Exploration, Exploitation, gamma +....) so that the operation of the controlled object is optimized

Excellent 4: You know how to tune a multilayer neural network using the PPO algorithm so that the given actuator behaves as desired.

Evaluation criteria, excellent (5)

Excellent 5: In addition to the previous ones, you understand the limitations of artificial intelligence in various practical applications and understand that OpenAI-Gym models require a strong substance control. You also know how to document your work well, using TensorFlow outputs to evaluate hyperparameters.

Prerequisites

Johdanto IoT-järjestelmiin

Objectives

Course objectives
In the course, you will learn how to use advanced new technologies, such as artificial intelligence, in the implementation of IoT systems. You get to know different neural networks and understand their basic parameters.

Competences:
EUR-ACE Knowledge and Understanding
EUR-ACE Engineering Practice


You know the architectures of neural networks and understand their application areas. You know how to make a Q-table of basic neural network design and implementation in practice. You know what the RL agent does and you know how to train the agent to make it work optimally. You understand the difference between On-Policy and Off-Policy algorithms and know how to use information to select an algorithm.

Content

Comparison of neural networks (RL, Supervised, Unsupervised )
Keras, Pytorch and other algorithm-based neural networks
RL agent and environment
Exploitation and exploration
Reward and hyperparameters
Basic neuromath and activation functions
Policy gradient method and value based learning
Bellman equation and its demo (Q-learning)
Pytorch, PPO algorithm and demo

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Avoidance 1: You understand the principles of neural networks and can apply the knowledge to select them

Satisfactory 2: You understand the basics of Q-learning methods and can make a small application with it

Evaluation criteria, good (3-4)

Good 3: Can adjust the parameter of the Bellman algorithm (Exploration, Exploitation, gamma +....) so that the operation of the controlled object is optimized

Excellent 4: You know how to tune a multilayer neural network using the PPO algorithm so that the given actuator behaves as desired.

Evaluation criteria, excellent (5)

Excellent 5: In addition to the previous ones, you understand the limitations of artificial intelligence in various practical applications and understand that OpenAI-Gym models require a strong substance control. You also know how to document your work well, using TensorFlow outputs to evaluate hyperparameters.

Prerequisites

Johdanto IoT-järjestelmiin

Objectives

Course objectives
In the course, you will learn how to use advanced new technologies, such as artificial intelligence, in the implementation of IoT systems. You get to know different neural networks and understand their basic parameters.

Competences:
EUR-ACE Knowledge and Understanding
EUR-ACE Engineering Practice


You know the architectures of neural networks and understand their application areas. You know how to make a Q-table of basic neural network design and implementation in practice. You know what the RL agent does and you know how to train the agent to make it work optimally. You understand the difference between On-Policy and Off-Policy algorithms and know how to use information to select an algorithm.

Content

Comparison of neural networks (RL, Supervised, Unsupervised )
Keras, Pytorch and other algorithm-based neural networks
RL agent and environment
Exploitation and exploration
Reward and hyperparameters
Basic neuromath and activation functions
Policy gradient method and value based learning
Bellman equation and its demo (Q-learning)
Pytorch, PPO algorithm and demo

Time and location

According to the schedule in state 431

Learning materials and recommended literature

Materials in the e-learning environment.

Teaching methods

- lectures
- independent study
- distance learning
- webinars
- small group learning
- exercises
- learning tasks
- seminars

Practical training and working life connections

- ekskursiot
- vierailijaluennot
- projektit

Alternative completion methods

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices

Student workload

One credit (1 Cr) corresponds to an average of 27 hours of work.

- lectures 52 h
- exercises 15 h
- assignment 35 h
- independent study 30 h
- company visits 3 h
Total 135 h

Further information for students

In the reference project, the robot car is trained to drive the track more evenly (small wobble). Reinforcement neural network optimizes car driving according to a reward, agent, policy. The AI ​​is located first on the server and finally on the edge of the network. Investigate whether driving improves as a result of edge calculation

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Avoidance 1: You understand the principles of neural networks and can apply the knowledge to select them

Satisfactory 2: You understand the basics of Q-learning methods and can make a small application with it

Evaluation criteria, good (3-4)

Good 3: Can adjust the parameter of the Bellman algorithm (Exploration, Exploitation, gamma +....) so that the operation of the controlled object is optimized

Excellent 4: You know how to tune a multilayer neural network using the PPO algorithm so that the given actuator behaves as desired.

Evaluation criteria, excellent (5)

Excellent 5: In addition to the previous ones, you understand the limitations of artificial intelligence in various practical applications and understand that OpenAI-Gym models require a strong substance control. You also know how to document your work well, using TensorFlow outputs to evaluate hyperparameters.

Prerequisites

Johdanto IoT-järjestelmiin

Objectives

Course objectives
In the course, you will learn how to use advanced new technologies, such as artificial intelligence, in the implementation of IoT systems. You get to know different neural networks and understand their basic parameters.

Competences:
EUR-ACE Knowledge and Understanding
EUR-ACE Engineering Practice


You know the architectures of neural networks and understand their application areas. You know how to make a Q-table of basic neural network design and implementation in practice. You know what the RL agent does and you know how to train the agent to make it work optimally. You understand the difference between On-Policy and Off-Policy algorithms and know how to use information to select an algorithm.

Content

Comparison of neural networks (RL, Supervised, Unsupervised )
Keras, Pytorch and other algorithm-based neural networks
RL agent and environment
Exploitation and exploration
Reward and hyperparameters
Basic neuromath and activation functions
Policy gradient method and value based learning
Bellman equation and its demo (Q-learning)
Pytorch, PPO algorithm and demo

Time and location

According to the schedule in state 431

Learning materials and recommended literature

Materials in the e-learning environment.

Teaching methods

- lectures
- independent study
- distance learning
- webinars
- small group learning
- exercises
- learning tasks
- seminars

Practical training and working life connections

- excursions
- visiting lecturers
- projects

Exam dates and retake possibilities

The possible date and method of the exam will be announced in the course opening.

Alternative completion methods

The admission procedures are described in the degree rule and the study guide. The teacher of the course will give you more information on possible specific course practices.

Student workload

One credit (1 Cr) corresponds to an average of 27 hours of work.

- lectures 52 h
- exercises 15 h
- assignment 35 h
- independent study 30 h
- company visits 3 h
Total 135 h

Further information for students

Evaluation according to the project

In the reference project, the robot car is trained to drive the track more evenly (small wobble). Reinforcement neural network optimizes car driving according to a reward, agent, policy. The AI ​​is located first on the server and finally on the edge of the network. Investigate whether driving improves as a result of edge calculation

Evaluation scale

0-5

Evaluation criteria, satisfactory (1-2)

Avoidance 1: You understand the principles of neural networks and can apply the knowledge to select them

Satisfactory 2: You understand the basics of Q-learning methods and can make a small application with it

Evaluation criteria, good (3-4)

Good 3: Can adjust the parameter of the Bellman algorithm (Exploration, Exploitation, gamma +....) so that the operation of the controlled object is optimized

Excellent 4: You know how to tune a multilayer neural network using the PPO algorithm so that the given actuator behaves as desired.

Evaluation criteria, excellent (5)

Excellent 5: In addition to the previous ones, you understand the limitations of artificial intelligence in various practical applications and understand that OpenAI-Gym models require a strong substance control. You also know how to document your work well, using TensorFlow outputs to evaluate hyperparameters.

Prerequisites

Johdanto IoT-järjestelmiin